Learning Latent Features With Infinite Nonnegative Binary Matrix Trifactorization

Nonnegative matrix factorization (NMF) has been widely exploited in many computational intelligence and pattern recognition problems. In particular, it can be used to extract latent features from data. However, previous NMF models often assume a fixed number of features, which are normally tuned and...

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Bibliographic Details
Published in:	IEEE transactions on emerging topics in computational intelligence Vol. 2; no. 6; pp. 450 - 463
Main Authors:	Yang, Xi, Huang, Kaizhu, Zhang, Rui, Hussain, Amir
Format:	Journal Article
Language:	English
Published:	Piscataway IEEE 01.12.2018 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:	Algorithms Artificial intelligence Bayes methods Bayesian analysis Benchmarks Clustering Clustering methods Complexity theory Computational intelligence Computational modeling Computer simulation Feature extraction Gaussian distribution Indian Buffet Process prior Infinite latent feature model Infinite non-negative binary matrix tri-factori-zation Iterative methods Learning Matrix decomposition Normal distribution Optimization Pattern recognition Simulation
ISSN:	2471-285X, 2471-285X
Online Access:	Get full text
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